In many instances, such as developing business strategy, investigating the effect of data dependencies on business developments can help prevent inaccurate estimations of risk and opportunity. Consequently, correlation is an important factor when analyzing data, and uncertain data in particular. However, correlation information is not always present in the data. In some cases, a correlation cannot be reliably computed using existing data. For example the data may be too sparse (e.g., data about the co-occurrence of extreme insurance claims). The handling of correlation, particularly the representation of correlation over continuous distributions, has only been addressed to a small extent in uncertain data management research.